The first tests of smartphone camera exposure effect on optical camera communication links

In this paper, we study the effect of smartphone camera exposure on the performance of optical camera communications (OCC) link. The exposure parameters of image sensor sensitivity (ISO), aperture and shutter speed are included. A static OCC link with a 8×8 red, green and blue (RGB) LED array employed as the transmitter and a smartphone camera as the receiver is demonstrated to verify the study. Signal-to-noise ratio (SNR) analysis at different ISO values, the effect of aperture and shutter speed on communication link quality is performed. While SNRs of 20.6 dB and 16.9 dB are measured at 1 m and 2 m transmission distance, respectively for a ISO value of 100, they are decreased to 17.4 dB and 13.32 dB for a ISO of 800. The bit error rate (BER) of a 1 m long OCC link with a camera’s shutter speed of 1/6000 s is 1.3×10 −3 (i.e., below the forward error correction BER limit of 3.8×10 −3 ) and is dropped to 0.0125 at a shutter speed of 1/20 s. This study provides insight of the basic smartphone settings and the exposure adjustment for further complex OCC links

Enhanced spectral modeling of sparse aperture imaging systems

The remote sensing community continues to pursue advanced sensor designs and post processing techniques that improve upon the spatial quality of collected overhead imagery. Unfortunately, spaceborne applications frequently encounter launch vehicle fairing and weight constraints that limit the size of the primary aperture that can be utilized for a given application. Sparse aperture telescopes provide a potential avenue for overcoming some of the size and weight issues associated with deploying a large monolithic mirror system. These telescope systems are constructed of smaller subapertures which are phased to form a common image field and thereby synthesize a larger effective primary diameter to obtain higher spatial resolution than that achievable with a single subaperture. Much of the research conducted to date in this sparse aperture arena has focused on the panchromatic image quality performance of various optical configurations through approaches that make use of resampled, gray-scale imagery products. The research effort performed in conjunction with this dissertation focused on laying the groundwork for synthetic model-based approaches for evaluating the optical performance of sparse aperture collection systems with enhanced spectral fidelity and a polychromatic object scene. It entailed a fundamental investigation and demonstration of the first-principles physics required to model such imaging systems. This theoretical development ultimately led to the generation of a modeling concept that more rigorously addresses the spectral characteristics of classic sparse aperture optical configurations used in remote sensing applications. To demonstrate the proposed theoretical foundation, a proof-of-concept digital model was implemented that incorporates essential components of the fundamental physical processes involved with typical sparse aperture collection systems, including any potential spectral effects unique to these design configurations. In addition to modeling the detected imagery derived from the collection system, there was also an interest in exploring the quality implications of image restoration techniques typically required for sparse aperture imaging systems. Several variations of the classic Wiener-Helstrom filter were implemented and investigated in response to this research objective. The basic restoration methodologies pursued in this effort provide a foundation for research into more advanced techniques in the future. Finally, a top-level sensitivity study of the image quality performance of various sparse aperture pupil configurations subjected to varying levels of subaperture dephasing and/or aberrations was performed. This exploration of the trade space focused on a panchromatic detection scenario and attempted to bound the performance region where unique spectral quality issues are observed for the unconventional collection telescopes targeted through this research effort

Assessment of the CORONA series of satellite imagery for landscape archaeology: a case study from the Orontes valley, Syria

In 1995, a large database of satellite imagery with worldwide coverage taken from 1960 until 1972 was declassified. The main advantages of this imagery known as CORONA that made it attractive for archaeology were its moderate cost and its historical value. The main disadvantages were its unknown quality, format, geometry and the limited base of known applications. This thesis has sought to explore the properties and potential of CORONA imagery and thus enhance its value for applications in landscape archaeology. In order to ground these investigations in a real dataset, the properties and characteristics of CORONA imagery were explored through the case study of a landscape archaeology project working in the Orontes Valley, Syria. Present-day high-resolution IKONOS imagery was integrated within the study and assessed alongside CORONA imagery. The combination of these two image datasets was shown to provide a powerful set of tools for investigating past archaeological landscape in the Middle East. The imagery was assessed qualitatively through photointerpretation for its ability to detect archaeological remains, and quantitatively through the extraction of height information after the creation of stereomodels. The imagery was also assessed spectrally through fieldwork and spectroradiometric analysis, and for its Multiple View Angle (MVA) capability through visual and statistical analysis. Landscape archaeology requires a variety of data to be gathered from a large area, in an effective and inexpensive way. This study demonstrates an effective methodology for the deployment of CORONA and IKONOS imagery and raises a number of technical points of which the archaeological researcher community need to be aware of. Simultaneously, it identified certain limitations of the data and suggested solutions for the more effective exploitation of the strengths of CORONA imagery

Error characterization of spectral products using a factorial designed experiment

The main objective of any imaging system is to collect information. Information is conveyed in remotely sensed imagery by the spatial and spectral distribution of the energy reflected/emitted from the earth. This energy is subsequently captured by an overhead imaging system. Post-processing algorithms, which rely on this spectral and spatial energy distribution, allow us to extract useful information from the collected data. Typically, spectral processing algorithms include such procedures as target detection, thematic mapping and spectral pixel unmixing. The final spectral products from these algorithms include detection maps, classification maps and endmember fraction maps. The spatial resolution, spectral sampling and signal-to-noise characteristics of a spectral imaging system share a strong relationship with one another based on the law of conservation of energy. If any one of these initial image collection parameters were changed then we would expect the accuracy of the information derived from the spectral processing algorithms to also change. The goal of this thesis study was to investigate the accuracy and effectiveness of spectral processing algorithms under different image levels of spectral resolution, spatial resolution and noise. In order to fulfill this goal a tool was developed that degrades hyperspectral images spatially, spectrally and by adding spectrally correlated noise. These degraded images were then subjected to several spectral processing algorithms. The information utility and error characterization of these degraded spectral products is assessed using algorithm-specific metrics. By adopting a factorial designed experimental approach, the joint effects of spatial resolution, spectral sampling and signal-to-noise with respect to algorithm performance was also studied. Finally, a quantitative performance comparison of the tested spectral processing algorithms was made

Investigation of techniques for inventorying forested regions. Volume 2: Forestry information system requirements and joint use of remotely sensed and ancillary data

The author has identified the following significant results. Effects of terrain topography in mountainous forested regions on LANDSAT signals and classifier training were found to be significant. The aspect of sloping terrain relative to the sun's azimuth was the major cause of variability. A relative insolation factor could be defined which, in a single variable, represents the joint effects of slope and aspect and solar geometry on irradiance. Forest canopy reflectances were bound, both through simulation, and empirically, to have nondiffuse reflectance characteristics. Training procedures could be improved by stratifying in the space of ancillary variables and training in each stratum. Application of the Tasselled-Cap transformation for LANDSAT data acquired over forested terrain could provide a viable technique for data compression and convenient physical interpretations

Mapping the shallow-water coral ecosystems of the Freely Associated States: an implementation plan

This Freely Associated States Shallow-water Coral Ecosystem Mapping Implementation Plan (FAS MIP) presents a framework for the development of shallow-water (~0–40 m; 0–22 fm) benthic habitat and possibly bathymetric maps of critical areas of the Freely Associated States (FAS). The FAS is made up of three self-governing groups of islands and atolls—the Republic of Palau (Palau), the Federated States of Micronesia (FSM), and the Republic of the Marshall Islands (RMI)—that are affiliated with the United States through Compacts of Free Association. This MIP was developed with extensive input from colleges, national and state regulatory and management agencies, federal agencies, non-governmental organizations, and individuals involved in or supporting the conservation and management of the FAS’s coral ecosystems. A list of organizations and individuals that provided input to the development of this MIP is provided in Appendix 1. This MIP has been developed to complement the Coral Reef Mapping Implementation Plan (2nd Draft) released in 1999 by the U.S. Coral Reef Task Force’s Mapping and Information Synthesis Working Group. That plan focused on mapping United States and FAS shallow-water (then defined as <30 m) coral reefs by 2009, based on available funding and geographic priorities, using primarily visual interpretation of aerial photography and satellite imagery. This MIP focuses on mapping the shallow-water (now defined as 0–40 m, rather than 0–30 m) coral ecosystems of the FAS using a suite of technologies and map development procedures. Both this FAS MIP and the 1999 Coral Reef Mapping Implementation Plan (2nd Draft) support to goals of the National Action Plan to Conserve Coral Reefs (U.S. Coral Reef Task Force, 2000). This FAS MIP presents a framework for mapping the coral ecosystems of the FAS and should be considered an evolving document. As priorities change, funding opportunities arise, new data are collected, and new technologies become available, the information presented herein will change

Optimisation of CT protocols for cardiac imaging using three-dimensional printing technology

Objective: This thesis investigates the application of 3D-printing technology for optimising coronary CT angiography (CCTA) protocols using iterative reconstruction (IR) as a dose optimisation strategy. Methods: In phase one, a novel 3D-printed cardiac insert phantom for the Lungman phantom was developed. The attenuation values of the printed phantom were compared to CCTA patients and Catphan® 500 images. In phase two, the printed phantom was scanned at multiple dose levels, and the datasets were reconstructed using different IR strengths. The image quality characteristics were measured to determine the dose reduction potential. In phase three, the influence of IR strengths with low-tube voltage for dose optimisation studies was investigated. The printed phantom and the Catphan® 500 were scanned at different tube currents and voltages. The results were compared to the patient datasets to measure the agreement between the phantoms and patient datasets. Results: In phase one, the attenuation values were consistent between the printed phantom, patient and Catphan® 500 images. In phase two, the results showed that decreasing dose levels had significantly increased the image noise (p<0.001). The application of various IR strengths had yielded a stepwise improvement of noise image quality with a dose reduction potential of up to 40%. In phase three, the results showed a significant interaction between the effects of low-tube voltage and the IR strengths on image quality (all p<0.001) but not the attenuation values. The mean differences were small between the patient-phantom datasets. The optimised CT protocols allowed up to 57% dose reduction in CCTA protocols while maintaining the image quality. Conclusions: The 3D-printed cardiac insert phantom can be used to evaluate the effect of using IR on dose reduction and image quality. This thesis proposes and validates a new method of developing phantoms for CCTA dose optimisation studies

Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA

Recent emphasis has been placed on merging regional forest inventory data with satellite-based information both to improve the efficiency of estimates of population totals, and to produce regional maps of forest variables. There are numerous ways in which forest class and structure variables may be modeled as functions of remotely sensed variables, yet surprisingly little work has been directed at surveying modem statistical techniques to determine which tools are best suited to the tasks given multiple objectives and logistical constraints. Here, a series of analyses to compare nonlinear and nonparametric modeling techniques for mapping a variety of forest variables, and for stratification of field plots, was conducted using data in the Interior Western United States. The analyses compared four statistical modeling techniques for predicting two discrete and four continuous forest inventory variables. The modeling techniques include generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Alternative stratification schemes were also compared for estimating population totals. The analyses were conducted within six ecologically different regions using a variety of satellite-based predictor variables. The work resulted in the development of an objective modeling box that automatically models spatial response variables as functions of any assortment of predictor variables through the four nonlinear or nonparametric modeling techniques. In comparing the different modeling techniques, all proved themselves workable in an automated environment, though ANNs were more problematic. When their potential mapping ability was explored through a simple simulation, tremendous advantages were seen in use of MARS and ANN for prediction over GAMs, CART, and a simple linear model. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS performed (marginally) best most often for binary variables, while GAMs performed (marginally) best most often for continuous variables. After considering a subjective ease of use measure, computing time and other predictive performance measures, it was determined that MARS had many advantages over other modeling techniques. In addition, stratification tests illustrated cost-effective means to improve precision of estimates of forest population totals. Finally, the general effect of map accuracy on the relative precision of estimates of population totals obtained under simple random sampling compared to that obtained under stratified random sampling was established and graphically illustrated as a tool for management decisions

An Evaluation of multispectral earth-observing multi-aperture telescope designs for target detection and characterization

Earth-observing satellites have fundamental size and weight design limits since they must be launched into space. These limits serve to constrain the spatial resolutions that such imaging systems can achieve with traditional telescope design strategies. Segmented and sparse-aperture imaging system designs may offer solutions to this problem. Segmented and sparse-aperture designs can be viewed as competing technologies; both approaches offer solutions for achieving finer resolution imaging from space. Segmented-aperture systems offer greater fill factor, and therefore greater signal-to-noise ratio (SNR), for a given encircled diameter than their sparse aperture counterparts, though their larger segments often suffer from greater optical aberration than those of smaller, sparse designs. Regardless, the use of any multi-aperture imaging system comes at a price; their increased effective aperture size and improvement in spatial resolution are offset by a reduction in image quality due to signal loss (less photon-collecting area) and aberrations introduced by misalignments between individual sub-apertures as compared with monolithic collectors. Introducing multispectral considerations to a multi-aperture imaging system further starves the system of photons and reduces SNR in each spectral band. This work explores multispectral design considerations inherent in 9-element tri-arm sparse aperture, hexagonal-element segmented aperture, and monolithic aperture imaging systems. The primary thrust of this work is to develop an objective target detection-based metric that can be used to compare the achieved image utility of these competing multi-aperture telescope designs over a designated design parameter trade space. Characterizing complex multi-aperture system designs in this way may lead to improved assessment of programmatic risk and reward in the development of higher-resolution imaging capabilities. This method assumes that the stringent requirements for limiting the wavefront error (WFE) associated with multi-aperture imaging systems when producing imagery for visual assessment, can be relaxed when employing target detection-based metrics for evaluating system utility. Simple target detection algorithms were used to determine Receiver Operating Characteristic (ROC) curves for the various simulated multi-aperture system designs that could be used in an objective assessment of each system\u27s ability to support target detection activities. Also, a set of regressed equations was developed that allow one to predict multi-aperture system target detection performance within the bounds of the designated trade space. Suitable metrics for comparing the shapes of two individual ROC curves, such as the total area under the curve (AUC) and the sample Pearson correlation coefficient, were found to be useful tools in validating the predicted results of the trade space regression models. And lastly, some simple rules of thumb relating to multi-aperture system design were identified from the inspection of various points of equivalency between competing system designs, as determined from the comparison metrics employed. The goal of this work, the development of a process for simulating multi-aperture imaging systems and comparing them in terms of target detection tasks, was successfully accomplished. The process presented here could be tailored to the needs of any specific multi-aperture development effort and used as a tool for system design engineers